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001    870951046 
003    OCoLC 
005    20200814215128.2 
006    m     o  d         
007    cr |n||||||||| 
008    140222s2014    xx      o     000 0 eng d 
020    9781118762509 
020    1118762509 
035    (OCoLC)870951046 
035    Ebook Central DDA Titles 
035    skip4alma 
040    EBLCP|beng|epn|cEBLCP|dOCLCQ|dOCLCO|dOCLCQ|dE7B|dRECBK
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049    txum 
050  4 QV771.4eb 
050  4 R853.C55eb 
100 1  O'Kelly, Michael. 
245 10 Clinical Trials with Missing Data :|ba Guide for 
       Practitioners. 
260    Hoboken :|bWiley,|c2014. 
300    1 online resource (473 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Statistics in Practice 
500    3.8.2 US guidance on considerations when research 
       supported by office of human research protections is 
       discontinued. 
505 0  Clinical Trials with Missing Data; Contents; Preface; 
       References; Acknowledgments; Notation; Table of SAS code 
       fragments; Contributors; 1 Whats the problem with missing 
       data?; 1.1 What do we mean by missing data?; 1.1.1 
       Monotone and non-monotone missing data; 1.1.2 Modeling 
       missingness, modeling the missing value and ignorability; 
       1.1.3 Types of missingness (MCAR, MAR and MNAR); 1.1.4 
       Missing data and study objectives; 1.2 An illustration; 
       1.3 Why cant I use only the available primary endpoint 
       data?; 1.4 Whats the problem with using last observation 
       carried forward? 
505 8  1.5 Can we just assume that data are missing at random?1.6
       What can be done if data may be missing not at random?; 
       1.7 Stress-testing study results for robustness to missing
       data; 1.8 How the pattern of dropouts can bias the 
       outcome; 1.9 How do we formulate a strategy for missing 
       data?; 1.10 Description of example datasets; 1.10.1 
       Example dataset in Parkinsons disease treatment; 1.10.2 
       Example dataset in insomnia treatment; 1.10.3 Example 
       dataset in mania treatment; Appendix 1.A: Formal 
       definitions of MCAR, MAR and MNAR; References; 2 The 
       prevention of missing data; 2.1 Introduction. 
505 8  2.2 The impact of "too much" missing data2.2.1 Example 
       from human immunodeficiency virus; 2.2.2 Example from 
       acute coronary syndrome; 2.2.3 Example from studies in 
       pain; 2.3 The role of the statistician in the prevention 
       of missing data; 2.3.1 Illustrative example from HIV; 2.4 
       Methods for increasing subject retention; 2.5 Improving 
       understanding of reasons for subject withdrawal; 
       Acknowledgments; Appendix 2.A: Example protocol text for 
       missing data prevention; Section X Subject retention; 
       References; 3 Regulatory guidance -- a quicktour. 
505 8  3.1 International conference on harmonization guideline: 
       Statistical principles for clinical trials: E93.2 The US 
       and EU regulatory documents; 3.3 Key points in the 
       regulatory documents on missingdata; 3.4 Regulatory 
       guidance on particular statistical approaches; 3.4.1 
       Available cases; 3.4.2 Single imputation methods; 3.4.3 
       Methods that generally assume MAR; 3.4.4 Methods that are 
       used assuming MNAR; 3.5 Guidance about how to plan for 
       missing data inastudy; 3.6 Differences in emphasis between
       the NRC report and EU guidance documents; 3.6.1 The term 
       "conservative." 
505 8  3.6.2 Last observation carried forward3.6.3 Post hoc 
       analyses; 3.6.4 Non-monotone or intermittently missing 
       data; 3.6.5 Assumptions should be readily interpretable; 
       3.6.6 Study report; 3.6.7 Training; 3.7 Other technical 
       points from the NRC report; 3.7.1 Time-to-event analyses; 
       3.7.2 Tipping point sensitivity analyses; 3.8 Other US/EU/
       international guidance documents that refer to missing 
       data; 3.8.1 Committee for medicinal products for human use
       guideline on anti-cancer products, recommendations on 
       survival analysis. 
506    Available only to authorized UTEP users. 
520    This book provides practical guidance for statisticians, 
       clinicians, and researchers involved in clinical trials in
       the biopharmaceutical industry, medical and public health 
       organisations. Academics and students needing an 
       introduction to handling missing data will also find this 
       book invaluable. The authors describe how missing data can
       affect the outcome and credibility of a clinical trial, 
       show by examples how a clinical team can work to prevent 
       missing data, and present the reader with approaches to 
       address missing data effectively. The book is illustrated 
       throug. 
588 0  Print version record. 
650  0 Clinical trials. 
650  0 Clinical trials|xStatistical methods. 
650 12 Clinical Trials as Topic. 
650 22 Bias (Epidemiology) 
650 22 Models, Statistical. 
650 22 Research Design. 
700 1  Ratitch, Bohdana. 
776 08 |iPrint version:|aO'Kelly, Michael.|tClinical Trials with 
       Missing Data : A Guide for Practitioners.|dHoboken : Wiley,
       ©2014|z9781118460702 
830  0 Statistics in practice. 
856 40 |uhttp://0-ebookcentral.proquest.com.lib.utep.edu/lib/utep
       /detail.action?docID=1636082|zTo access this resource 
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